Distributed Constraint Optimization Problems (DCOPs) offer a powerful approach for the description and resolution of cooperative multi-agent problems. In this model, a group of agents coordinates their actions to optimize a global objective function, taking into account their preferences or constraints. A core limitation of this model is the assumption that the preferences of all agents or the costs of all constraints are specified a priori. Unfortunately, this assumption does not hold in a number of application domains where preferences or constraints must be elicited from the users. One of such domains is the Smart Home Device Scheduling (SHDS) problem, which is motivated by the aforementioned limitation. The goal of this line of research is to develop general models for preference elicitation in DCOPs, propose several heuristics to elicit preferences in DCOPs, and empirically evaluate the effect of these heuristics on different real-world and synthetic benchmarks such as SHDS and random binary graphs.